[CI]: Add LMCache Unit Tests

.buildkite/test-amd.yaml

@@ -336,6 +336,8 @@ steps:
     - vllm/
     - tests/v1
   commands:
+    # Install kv connector dependencies for integration tests (needed for test collection)
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     # split the test to avoid interference
     - pytest -v -s -m 'not cpu_test' v1/core
     - pytest -v -s v1/executor

.buildkite/test-pipeline.yaml

@@ -315,6 +315,8 @@ steps:
     - vllm/
     - tests/v1
   commands:
+    # Install kv connector dependencies for integration tests (needed for test collection)
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     # split the test to avoid interference
     - pytest -v -s -m 'not cpu_test' v1/core
     - pytest -v -s v1/executor

tests/v1/kv_connector/unit/test_lmcache_integration.py

@@ -0,0 +1,271 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# NOTE: if your PR has broken one of the tests here (sorry),
+# kindly patch the corresponding integration in
+# /vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/vllm_v1_adapter.py
+# or reach out to @aposataC for assistance
+
+# Assumption vs. Correctness Tests:
+# these unit tests do *not* test correctness of LMCache-side or vLLM-side logic
+# it is to ensure that assumptions LMCache makes about vLLM's interface are stable
+def assumes(obj, attr, is_callable=False, is_instance_of=None):
+    import inspect
+    from dataclasses import is_dataclass
+
+    assumption_msg = f"LMcache currently assumes that {obj} has a(n) {attr} attribute"
+    if hasattr(obj, attr):
+        attr_value = getattr(obj, attr)
+    elif is_dataclass(obj) and attr in getattr(obj, "__dataclass_fields__", {}):
+        field = obj.__dataclass_fields__[attr]
+        field_type = field.type
+        origin = getattr(field_type, "__origin__", None)
+        if origin is not None:
+            field_type = origin
+        attr_value = field_type
+    else:
+        raise AssertionError(assumption_msg)
+    if is_callable:
+        assumption_msg += f" and that {obj}.{attr} is a callable"
+        assert callable(attr_value), assumption_msg
+    if is_instance_of:
+        assumption_msg += f" and that {obj}.{attr} is an instance of {is_instance_of}"
+        if isinstance(attr_value, property):
+            fget = attr_value.fget
+            assert fget is not None, f"Property {obj}.{attr} has no fget"
+            sig = inspect.signature(fget)
+            ret_anno = sig.return_annotation
+            assert ret_anno is not inspect._empty, (
+                f"Property {obj}.{attr} has no return annotation"
+            )
+            assert ret_anno == is_instance_of, assumption_msg
+        else:
+            if isinstance(attr_value, type):
+                assert attr_value is is_instance_of, assumption_msg
+            else:
+                assert isinstance(attr_value, is_instance_of), assumption_msg
+
+
+def test_multimodal_interface():
+    # protect against interface changes
+    from vllm.multimodal.inputs import PlaceholderRange
+
+    assumes(PlaceholderRange, "offset")
+    assumes(PlaceholderRange, "length")
+
+    # test a minimal case
+    import torch
+
+    from vllm.distributed.kv_transfer.kv_connector.v1.lmcache_integration.utils import (
+        apply_mm_hashes_to_token_ids,
+    )
+
+    token_ids = torch.arange(10, dtype=torch.long)
+    mm_hashes = ["0000", "1111"]  # hex repr of 0 and 4369
+    mm_positions = [
+        PlaceholderRange(offset=0, length=4),
+        PlaceholderRange(offset=5, length=4),
+    ]
+    apply_mm_hashes_to_token_ids(token_ids, mm_hashes, mm_positions)
+    assert token_ids.tolist() == [0, 0, 0, 0, 4, 4369, 4369, 4369, 4369, 9]
+
+
+def test_config_interface():
+    # protect against interface changes
+    from vllm.config import VllmConfig
+    from vllm.config.cache import CacheConfig
+    from vllm.config.kv_transfer import KVTransferConfig
+    from vllm.config.model import ModelConfig
+    from vllm.config.parallel import ParallelConfig
+
+    assumes(VllmConfig, "model_config")
+    assumes(VllmConfig, "cache_config")
+    assumes(VllmConfig, "parallel_config")
+    assumes(VllmConfig, "kv_transfer_config")
+
+    assumes(KVTransferConfig, "kv_role")
+    assumes(KVTransferConfig, "kv_connector_extra_config")
+
+    assumes(ModelConfig, "use_mla", is_instance_of=bool)
+    assumes(ModelConfig, "dtype")
+    assumes(ModelConfig, "max_model_len")
+    assumes(ModelConfig, "get_vocab_size", is_callable=True)
+    assumes(ModelConfig, "get_num_attention_heads", is_callable=True)
+    assumes(ModelConfig, "get_num_kv_heads", is_callable=True)
+    assumes(ModelConfig, "get_head_size", is_callable=True)
+    assumes(ModelConfig, "get_num_layers", is_callable=True)
+    assumes(ModelConfig, "get_num_kv_heads", is_callable=True)
+    assumes(ModelConfig, "model")
+
+    assumes(ParallelConfig, "world_size")
+    assumes(ParallelConfig, "rank")
+    assumes(ParallelConfig, "tensor_parallel_size")
+    assumes(ParallelConfig, "pipeline_parallel_size")
+    assumes(ParallelConfig, "data_parallel_size_local")
+    assumes(ParallelConfig, "data_parallel_rank_local")
+
+    assumes(CacheConfig, "cache_dtype")
+    assumes(CacheConfig, "block_size")
+    assumes(CacheConfig, "gpu_memory_utilization")
+    assumes(CacheConfig, "swap_space")
+    assumes(CacheConfig, "enable_prefix_caching")
+
+    # mla metadata minimal cases
+    from vllm.distributed.kv_transfer.kv_connector.v1.lmcache_integration.utils import (
+        mla_enabled,
+    )
+
+    model_config = ModelConfig(model="deepseek-ai/DeepSeek-R1")
+    assert mla_enabled(model_config)
+    model_config = ModelConfig(model="Qwen/Qwen3-0.6B")
+    assert not mla_enabled(model_config)
+
+    # kv metadata minimal case
+    from vllm.utils.torch_utils import get_kv_cache_torch_dtype
+
+    model_config = ModelConfig(dtype="bfloat16")
+    parallel_config = ParallelConfig()
+    cache_config = CacheConfig(cache_dtype="bfloat16")
+    kv_dtype = get_kv_cache_torch_dtype(cache_config.cache_dtype, model_config.dtype)
+    use_mla = mla_enabled(model_config)
+    chunk_size = 256
+    num_layer = model_config.get_num_layers(parallel_config)
+    num_kv_head = model_config.get_num_kv_heads(parallel_config)
+    head_size = model_config.get_head_size()
+    kv_shape = (num_layer, 1 if use_mla else 2, chunk_size, num_kv_head, head_size)
+
+    # dummy lmcache metadata creation example
+    _ = (
+        model_config.model,
+        parallel_config.world_size,
+        parallel_config.rank,
+        "vllm",
+        kv_dtype,
+        kv_shape,
+        use_mla,
+    )
+
+
+def test_request_interface():
+    # protect against interface changes
+    from types import NoneType
+
+    from vllm.sampling_params import SamplingParams
+    from vllm.v1.request import Request
+
+    req = Request(
+        request_id="test_request",
+        prompt_token_ids=[1, 2, 3],
+        sampling_params=SamplingParams(max_tokens=10),
+        pooling_params=None,
+        eos_token_id=100,
+        lora_request=None,
+    )
+    assumes(req, "mm_features", is_instance_of=(list, NoneType))
+    assumes(req, "request_id")
+    assumes(req, "priority")
+    assumes(req, "prompt_token_ids")
+    assumes(req, "sampling_params")
+    assumes(req, "num_tokens")
+    assumes(req, "kv_transfer_params", is_instance_of=(dict, NoneType))
+
+    from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalKwargsItem
+
+    assumes(MultiModalFeatureSpec, "identifier")
+    assumes(MultiModalFeatureSpec, "mm_position")
+
+    # minimal case:
+    from vllm.multimodal.inputs import PlaceholderRange
+
+    request = Request(
+        request_id="test_request",
+        prompt_token_ids=[1, 2, 3],
+        sampling_params=SamplingParams(max_tokens=10),
+        pooling_params=None,
+        eos_token_id=100,
+        lora_request=None,
+        mm_features=[
+            MultiModalFeatureSpec(
+                modality="image",
+                identifier="0000",
+                data=MultiModalKwargsItem.dummy("dummy_m"),
+                mm_position=PlaceholderRange(offset=0, length=10),
+            )
+        ],
+    )
+
+    from vllm.distributed.kv_transfer.kv_connector.v1.lmcache_integration.utils import (
+        extract_mm_features,
+    )
+
+    mm_hashes, mm_positions = extract_mm_features(request)
+    assert isinstance(mm_hashes, list)
+    assert len(mm_hashes) == 1
+    assert isinstance(mm_positions, list)
+    assert len(mm_positions) == 1
+    assert mm_positions[0].offset == 0
+    assert mm_positions[0].length == 10
+
+
+def test_new_request_interface():
+    # protect against interface changes
+    from vllm.v1.core.sched.output import NewRequestData
+
+    assumes(NewRequestData, "req_id")
+    assumes(NewRequestData, "block_ids")
+    assumes(NewRequestData, "prompt_token_ids")
+    assumes(NewRequestData, "sampling_params")
+
+
+def test_sampling_params_interface():
+    # protect against interface changes
+    from vllm.sampling_params import SamplingParams
+
+    assumes(SamplingParams, "extra_args")
+
+    # dumb example use case in LMCache
+    kv_transfer_params = {
+        "lmcache.tag.user": "example_user_1",
+        "lmcache.ttl": 60,
+    }
+    sampling_params = SamplingParams(
+        extra_args={"kv_transfer_params": kv_transfer_params}
+    )
+    assert sampling_params.extra_args["kv_transfer_params"] == kv_transfer_params
+
+
+def test_tp_interface():
+    # protect against interface changes
+    import inspect
+
+    from vllm.distributed.parallel_state import get_tp_group
+
+    sig = inspect.signature(get_tp_group)
+    GroupCoordinator = sig.return_annotation
+
+    assumes(GroupCoordinator, "broadcast", is_callable=True)
+    assumes(GroupCoordinator, "broadcast_object", is_callable=True)
+
+
+def test_forward_context_interface():
+    # protect against interface changes
+    from vllm.forward_context import ForwardContext
+
+    assumes(ForwardContext, "no_compile_layers", is_instance_of=dict)
+    assumes(ForwardContext, "virtual_engine")
+    assumes(ForwardContext, "attn_metadata")
+
+
+def test_scheduler_output_interface():
+    # protect against interface changes
+    from vllm.v1.core.sched.output import SchedulerOutput
+
+    assumes(SchedulerOutput, "finished_req_ids")
+    assumes(SchedulerOutput, "scheduled_new_reqs", is_instance_of=list)
+    assumes(SchedulerOutput, "num_scheduled_tokens", is_instance_of=dict)
+    assumes(SchedulerOutput, "scheduled_cached_reqs")
+
+    from vllm.v1.core.sched.output import CachedRequestData
+
+    assumes(CachedRequestData, "req_ids", is_instance_of=list)
+    assumes(CachedRequestData, "new_block_ids", is_instance_of=list)

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/vllm_v1_adapter.py

@@ -724,7 +724,7 @@ def get_inference_info(self) -> dict:
                 "max_model_len": getattr(
                     vllm_config.model_config, "max_model_len", None
                 ),
-                "vocab_size": getattr(vllm_config.model_config, "vocab_size", None),
+                "vocab_size": vllm_config.model_config.get_vocab_size(),
                 "num_layers": getattr(
                     vllm_config.model_config, "get_num_layers", lambda _: None
                 )(vllm_config.parallel_config),